In the field of computer vision, one of the utilities of such a location sensitive error metric can be in tasks involving image reconstruction.
Typically for such tasks, the difference between a reconstructed image and the original image is computed to iteratively improve the reconstruction  [1]}, [2]} and/or make a downstream classification [3]}, [4]}.
To empirically evaluate our hypothesis, we examine proximally sensitive error against mean squared error for the tasks of image anomaly detection and unsupervised pre-training, both involving image reconstruction.
